KaydedilenlerDerginizi ekleyin

Dergiler / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2019 / Cilt: 37 - Sayı: 3

CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS

Damla ILTER
Ozan KOCADAGLI
Sayfa
855–870
Yayın Tarihi
2020-09-01
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In the implementations, the proposed procedure is applied to a couple of benchmark credit scoring data sets and its performance is compared with traditional approaches.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The credit scoring is one of the major activities in the banking sector.
📊
Temel Bulgular & Veri:For this reason, artificial intelligence techniques are mostly preferred to handle the credit scoring problems accurately.
💡
Sonuç & Katkı:In the implementations, the proposed procedure is applied to a couple of benchmark credit scoring data sets and its performance is compared with traditional approaches.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Credit scoring · artificial neural networks · cross-entropy

Abstract

The credit scoring is one of the major activities in the banking sector. Because of growing market and increasing the loan applications, this field still continues its concern in terms of rating the applicants and assessing the credit amounts. To reduce the number of wrong decisions in the credit evaluation process, the decision makers focus on estimating more robust models. However, the traditional methods are criticized due to various pre-requisites and linear approximations in the high dimensional and excessive nonlinear cases. For this reason, artificial intelligence techniques are mostly preferred to handle the credit scoring problems accurately. This study presents an efficient procedure that is based on ANNs with cross-entropy and fuzzy relations in the context of the credit scoring. In the implementations, the proposed procedure is applied to a couple of benchmark credit scoring data sets and its performance is compared with traditional approaches.

🔖

Bu Makaleye Atıf Yap

ILTER, D., KOCADAGLI, O. (2020). CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 37 - Sayı: 3, 855–870. https://ajindex.com/articles/419155

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (16)

  1. D. Soydaner, O. Kocadağlı, Artificial neural networks with gradient learning algorithm for credit scoring, Istanbul University Journal of the School of Business, 44(2) (2015) 3-12
  2. N. C. Hsieh, An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications, 27(4) (2004) 623-633
  3. X. Zhu, J. Li, D. Wu, H. Wang, C. Liang, Balancing accuracy, complexity and interpretability in consumer credit decision making: A C-TOPSIS classification approach, Knowledge-Based Systems, 52 (2013) 258-267
  4. F. L. Chen, F. C. Li, Combination of feature selection approaches with SVM in credit scoring, Expert Systems with Applications, 37(7) (2010) 4902-4909
  5. G. Wang, J. Hao, J. Ma, H. Jiang, A comparative assessment of ensemble learning for credit scoring, Expert Systems with Applications, 38(1) (2011) 223-230
  6. K. Bijak, L. C. Thomas, Does segmentation always improve model performance in credit scoring?, Expert Systems with Applications, 39(3) (2012) 2433–2442
  7. B. Baesens, R. Setiono, C. Mues, J. Vanthienen, Using neural network rule extraction and decision tables for credit-risk evaluation, Management Science, 49(3) (2003) 312-329
  8. J. R. Quinlan, C4.5: Programs for machine learning, San Francisco, CA, USA, 1993
  9. M. G. Tsipouras, T. P. Exarchos, D. I. Fotiadis, A methodology for automated fuzzy model generation, Fuzzy Sets and Systems, 159(23) (2008) 3201-3220
  10. T. Harris, Credit scoring using the clustered support vector machine, Expert Systems with Applications, 42(2) (2015) 741–750
  11. J. M. Tomczak, M. Zieba, Classification restricted boltzmann machine for comprehensible credit scoring model, Expert System Applications, 42(4) (2015) 1789– 1796
  12. F. C. Tsai, Combining cluster analysis with classifier ensembles to predict financial distress, Information Fusion, 16 (2014) 46-58
  13. A. B. Hens, M. K. Tiwari, Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method, Expert Systems with Applications, 39(8) (2012) 6774–6781
  14. W. Chen, C. Ma, L. Ma, Mining the customer credit using hybrid support vector machine technique, Expert Systems with Applications, 36(4), (2009) 7611–7616
  15. S. T. Lou, B. W. Cheng, C. H. Hsieh, Prediction model building with clustering-launched classification and support vector machines in credit scoring, Expert Systems with Applications, 36(4) (2009) 7562-7566
  16. C. L. Hua